Introduction: COVID-19 generated a system-wide shock causing an unbalanced equilibrium between producing adequately trained physicians and meeting extraordinary operational needs. Previous studies report the experience of surgical residents during COVID-19 at a regional level. This study measures the learning losses related with the redeployment of highly specialized medical professionals to the care of COVID-19 patients, while we systematically investigate proposed remedial strategies.
Methods: We administered an online cross-sectional survey in 67 countries capturing training inputs (i.e., surgeries and seminars residents participated in) before and during the pandemic and retrieved residents' expected learning outputs, career prospects and recommended remedial measures for learning losses. We compared responses of residents working in (treatment group) and out (control group) of hospitals with COVID-19 patients.
Results: The analysis included 432 plastic surgery residents who were in training during the pandemic. Most of the learning losses were found in COVID-19 hospitals with 37% and 16% loss of surgeries and seminars, respectively, per week. Moreover, 74%, 44%, and 55% of residents expected their surgical skill, scientific knowledge, and overall competence, respectively, to be lower than those of residents who graduated before COVID. Residents in COVID-19 hospitals reported participating in significantly (P < 0.001) fewer surgeries and having significantly (P < 0.001) lower surgical skill relative to those not in COVID-19 hospitals.
Conclusions: The perceived lower competence and the fall-off in surgical skill and scientific knowledge among future surgeons suggest that health-care systems globally may have limited capacity to perform specialized and costly procedures in the future.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.jss.2024.04.028 | DOI Listing |
Heliyon
January 2025
Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran.
Lost circulation is one of the important challenges in drilling operations and bears financial losses and operational risks. The prime causes of lost circulation are related to several geological parameters, especially in problem-prone formations. Herein, the approach of applying machine learning models to forecast the intensity of lost circulation using well-log data is presented in this work.
View Article and Find Full Text PDFSpectrochim Acta A Mol Biomol Spectrosc
December 2024
College of Electrical Engineering, Xinjiang University, Urumqi 830046, China. Electronic address:
As a natural oil, horse oil has unique biological activity ingredients and therapeutic characteristics, which has important application value and market potential in healthcare, food, skin care and other fields. However, fraud is rampant in the horse oil market, and traditional methods such as chemical analysis and physical property detection are time-consuming, costly, and have low accuracy in detecting adulteration. Excessive adulteration may cause health risks, skin problems, and economic losses.
View Article and Find Full Text PDFPLoS One
January 2025
Logistics service company, Civil Aviation Flight University of China, Guanghan, Sichuan, China.
The risk assessment and prevention in traditional airport safety assurance usually rely on human experience for analysis, and there are problems such as heavy manual workload, excessive subjectivity, and significant limitations. This article proposed a risk assessment and prevention mechanism for airport security assurance that integrated LSTM algorithm. It analyzed the causes of malfunctioning flights by collecting airport flight safety log datasets.
View Article and Find Full Text PDFSci Rep
January 2025
School of Information Engineering, Changji University, Changji, 831100, Xinjiang, China.
Healthcare insurance fraud imposes a significant financial burden on healthcare systems worldwide, with annual losses reaching billions of dollars. This study aims to improve fraud detection accuracy using machine learning techniques. Our approach consists of three key stages: data preprocessing, model training and integration, and result analysis with feature interpretation.
View Article and Find Full Text PDFSci Rep
January 2025
Brunel University of London, Uxbridge, UB8 3PH, UK.
Efficient energy management and maintaining an optimal indoor climate in buildings are critical tasks in today's world. This paper presents an innovative approach to surrogate modeling for predicting indoor air temperature (IAT) in buildings, leveraging advanced machine learning techniques. At the core of this study is the application of Long Short-Term Memory (LSTM) networks for time-series modeling, which significantly enhances the capture of temporal dependencies in temperature predictions.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!